Web Search Basics. Berlin Chen Department of Computer Science & Information Engineering National Taiwan Normal University
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1 Web Search Basics Berlin Chen Department of Computer Science & Information Engineering National Taiwan Normal University References: 1. Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press, (Chapters & associated slides) 2. Raymond J. Mooney s teaching materials 3. Lan Huang. A Survey on Web Information Retrieval Technologies. Available at: <
2 The World Wide Web (Web) Created in 1989 by Tim Berners-Lee at CERN (in Switzerland) An environment of accessing to interlinked and hypertext documents via the Internet Client-server design for transfer text, images, videos, and other multimedia, encoded with html (hypertext markup language), via a protocol (http, hypertext transfer protocol) The client side is usually a browser, a GUI environment, sending an http request to a web server (by specifying a URL, universal resource locator) Asynchronous communication domain IR Berlin Chen 2
3 Web Characteristics The Web No Control: democratization of creation and linking (publishing). Content includes truth, lies, obsolete information, contradictions Distributed Data: Documents spread over millions of different web servers Heterogeneity: Unstructured (text, html, ), semi-structured (XML, annotated photos), structured (databases) Variety of Languages: The types of languages used are more than 100 Large Volume: Scale much larger than previous text corpora (slowed down from initial volume doubling every few months but still expanding) Volatile Data: content can be dynamically generated and removed IR Berlin Chen 3
4 Rapid Proliferation of Web Content Total Web Sites Across All Domains August November 2007 ( 15M A large fraction of growth in sites has come from the increasing number of blogging sites (in particular at Live Spaces, Blogger and MySpace) in the recent past IR Berlin Chen 4
5 Internet Users by Languages End of 2004, total millions All Others, 12.70% Dutch, 1.70% Portuguese, 2.90% Italian, 3.60% Korean, 3.80% French, 4.40% Spanish, 6.70% German, 6.80% Japanese, 8.30% English, 35.90% Chinese, 13.20% IR Berlin Chen 5
6 Access to Web Content Full-text index search engines E.g., Google, Altavista, Excite, Infoseek, etc. Keyword search supported by inverted indexes and ranking mechanisms Manual hierarchical taxonomies (directories) populated with web pages in categories E.g., Yahoo!, Yam, etc. Human editors assemble a large hierarchically structured directory of web pages Users browse through trees of category labels IR Berlin Chen 6
7 Growth of Web Pages Indexed Billions of Pages Google Inktomi AllTheWeb Teoma Altavista SearchEngineWatch Link to Note from Jan 2004 Assuming 20KB per page, 1 billion pages is about 20 terabytes of data. This slide is adopted from Raymond J. Mooney s teaching materials IR Berlin Chen 7
8 Rate of Change for Web Pages Fetterly et al. study (2002): several views of data, 150 million pages over 11 weekly crawls Bucketed into 85 groups by extent of change IR Berlin Chen 8
9 Frequency of Using Search Engines IR Berlin Chen 9
10 User Query Needs (1/4) User query roughly fall into three categories Informational want to learn about something E.g., Taroko Navigational want to go to that page E.g., China Airlines Transactional want to do something (web-mediated) Purchasing a product, downloading a file or making a reservation Discern which of these categories a query falls into can be challenging! IR Berlin Chen 10
11 Ill-defined queries User Query Needs (2/4) Short 2001: 2.54 terms avg, 80% < 3 words 1998: 2.35 terms avg, 88% < 3 words Imprecise terms Suboptimal syntax Low effort Specific behavior 85% look over one result screen only (mostly above the fold) 78% of queries are not modified (one query/session) Wide variance in Needs Expectations Knowledge Bandwidth IR Berlin Chen 11
12 Query Distribution User Query Needs (3/4) 白血病 淋巴瘤 Power law: few popular broad queries, many rare specific queries IR Berlin Chen 12
13 User Query Needs (4/4) How far do people look for results? (Source: iprospect.com WhitePaper_2006_SearchEngineUserBehavior.pdf) IR Berlin Chen 13
14 Goal Web Search Engines (1/2) Return both high-relevance and high-quality (i.e., valuable) pages Given the heterogeneity of the Web and the ill-formed queries Architecture: main constituents Crawler Indexer Query Server Web? Crawler Query Repository Indexer Barrels Searcher PageRanker Ranked Query Server Documents/Pages IR Berlin Chen 14
15 Crawler Web Search Engines (2/2) Collect pages from the Web Done by distributed crawlers URL server sends lists of URL to be fetched by crawlers Store server compresses and stores pages (full HTML texts) into a repository Duplicate content detection Indexer Process the retrieved pages/documents and represent them in efficient search data structures (inverted files) Query server Accept the query from the user and return the result pages by consulting the search data structures IR Berlin Chen 15
16 Hyperlink and Anchor Text (1/2) Web as a Directed Graph - Two intuitions Hyperlinks from a web page as a form of conferral of authority I.e., A hyperlink between pages denotes author perceived relevance (quality signal) Page A Anchor hyperlink Page B The anchor (text) of the hyperlink describes the target page (textual context) A short summary of the target page <a href= > Journal of the ACM </a> IR Berlin Chen 16
17 Hyperlink and Anchor Text (2/2) When indexing a document D, include anchor text from links pointing to D a derogatory anchor text The evil empire for computer industry Armonk, NY-based computer giant IBM announced today Joe s computer hardware links Compaq HP IBM Big Blue today announced record profits for the quarter IR Berlin Chen 17
18 PageRank Algorithm Proposed by L. Page and Brain, 1998 Notations A page A has pages T 1 T n which point to it (citations) d range from 0~1, a damping factor (Google sets to be 0.85) C(A):Number of links going out of page A A PageRank of a page A PR ( A ) = ( 1 d ) + d PR C ( T1 ) ( T ) 1 + L + PR C ( T ) n ( T ) n PageRank of each page is randomly assigned at the initial iteration and its value tends to be saturated through iterations A page with a high PageRank value Many pages pointing to it Or, there are some pages that point to it and have high PageRank values IR Berlin Chen 18
19 Business Models for Web Search (1/3) Advertisers pay for banner ads (advertisements) on the site that do not depend on a user s query CPM: Cost Per Mille (thousand impressions). Pay for each ad display CPC: Cost Per Click. Pay only when user clicks on ad CTR: Click Through Rate. Fraction of ad impressions that result in clicks throughs. CPC = CPM / (CTR * 1000) CPA: Cost Per Action (Acquisition). Pay only when user actually makes a purchase on target site Advertisers bid for keywords. Ads for highest bidders displayed when user query contains a purchased keyword PPC: Pay Per Click. CPC for bid word ads (e.g. Google AdWords) This slide is adopted from Raymond J. Mooney s teaching materials IR Berlin Chen 19
20 Business Models for Web Search (2/3) Paid banner ads (news portal) Advertisements IR Berlin Chen 20
21 Business Models for Web Search (3/3) Bid keywords (search engine) Advertisements Algorithmic results IR Berlin Chen 21
22 Final Project Description Reference site: Contact TA for details of Corpus and Internet/Web Application Programs Project Due: 25 Jan IR Berlin Chen 22
Web Search Basics. Berlin Chen Department t of Computer Science & Information Engineering National Taiwan Normal University
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